The Role of Regulations in Decisions Regarding the Transition to Organic, Biodynamic, and Sustainable Agricultural Productions: The Case of Niagara Vineyards
Bibliographic record
Abstract
There is a myriad of motivations and barriers that influence agricultural producers to adopt ecologically-sound management practices.Therefore, this research aims to explore vintners' motivations to adopt organic, biodynamic, and sustainable practices in the Niagara region.This research also investigates what the challenges are in the transition process, and whether the regulations facilitate or hinder producers from completing the transition.This research employs a survey of regulations, semi-structured interviews, and online questionnaires to acquire data on vintners' experiences.The results conclude that Niagara's wineries are motivated to adopt ecologically-sound management practices due to environmental, social, and economic reasons.The results also indicate that there is a paradox of sustainability among organic, biodynamic, and sustainable producers on which practice is considered environmentally friendly.Lastly, the findings reveal that the wineries did not encounter severe challenges during transition, however, they note that the regulatory requirements and certification process need to improve.iii Dedication This thesis is dedicated to my mother, Dr. Cara MacMillan for her constant love, faith, support, and guidance.You are the reason why I am where I am today.Thank you for instilling into me your drive, work ethic, and love of learning.their knowledge and guidance as I tackle this research.You two have taught me how to be a better
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".